In Vivo Characterization of Changing Blood-Tumor Barrier Permeability in a Mouse Model of Breast Cancer Metastasis
Bibliographic record
Abstract
OBJECTIVES: The current lack of efficacy for any chemo- or molecular therapeutic in the treatment of brain metastases is thought to be due, in part, to the heterogeneous permeability of the blood-brain-barrier (BBB). Little is known about how heterogeneous permeability develops, or how it varies among individual metastases. Understanding the BBB's role in metastasis will be crucial to the development of new, more effective therapies. In this article, we developed the first magnetic resonance imaging-based strategy to detect and measure the volumes of BBB permeable and nonpermeable metastases and studied the development of altered BBB permeability in metastases in vivo, over time in a mouse model of breast cancer metastasis to the brain. MATERIALS AND METHODS: Animals bearing human experimental brain metastases of breast cancer (231-BR cells) were imaged, using 3-dimensional balanced steady-state free precession to visualize total metastases, and contrast-enhanced T1-weighted spin echo with gadopentetic acid (Gd-DTPA) to visualize which of these displayed contrast enhancement, as Gd-DTPA leakage is indicative of altered BBB permeability. RESULTS: Metastases detected 20 days after injection showed no Gd-DTPA enhancement. At day 25, 6.1% ± 6.3% (mean ± standard deviation) of metastases enhanced, and by day 30, 28.1% ± 14.2% enhanced (P < 0.05). Enhancing metastases (mid: 0.14 ± 0.18 mm, late: 0.24 ± 0.32 mm) had larger volumes than nonenhancing (mid: 0.04 ± 0.04 mm, late: 0.09 ± 0.09 mm, P < 0.05); however, there was no significant difference between the growth rates of the 2. CONCLUSIONS: A significant number of brain metastases were uniformly nonpermeable, which highlights the need for developing treatment strategies that can overcome the permeability of the BBB. The model developed herein can provide the basis for in vivo evaluation of both BBB permeable and nonpermeable metastases response to therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".